Alzheimer's disease (AD) is the most common neurodegenerative disease, afflicting over 4 million people over the age of 65 years, in the U.S. Current medications treat the symptoms but not the underlying causes of disease. There is therefore an urgent need to understand the pathogenic mechanisms of disease to enable rational drug design. During the last twenty years genetic studies of familial early onset AD have dramatically changed our understanding of the disease by demonstrating that mutations in three different genes cause disease via a common biochemical pathway involving B-amyloid (Ali) metabolism. Genetic epidemiology has demonstrated that late onset AD (LOAD) also has a strong genetic component. However, to date only the e4 allele of apolipoprotein E, present in only 50% of LOAD cases, has been convincingly demonstrated to influence risk for LOAD. There is therefore a clear need for new approaches to understanding the genetics of LOAD. We will use intermediate traits, or endophenotypes to identify novel genetic risk factors for LOAD. Endophenotypes may be continuous variables that are correlated with disease but measurable in many or all individuals, avoiding the heterogeneity associated with clinical diagnoses and allowing the use of quantitative statistical methods. Endophenotypes may also provide a biological model of disease and the possible effects of the associated genetic variation. Several promising endophenotypes are protein biomarkers found in cerebrospinal fluid (CSF) including amyloid-beta (A(3), tau, serpin peptidase inhibitor, clade C (antithrombin), member 1 (ATI11), serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 3 (ACT), carnosine dipeptidase 1 (CNDP1) andA-2- glycoprotein 1, zinc (ZAG). These proteins are present in all individuals, show variability amongnon- demented individuals and change with disease. The goal of this study is to identify cis-acting genetic variation that is associated with CSF levels of these AD biomarkers, and to test in independent datasets whether this variation also influences age at onset of AD or risk for AD. Functional studies will be employed to determine the biological effects of the associated genetic variation. These data will inform our models of age at onset of AD, AD diagnosis (project 2) and our studies of the interaction between preclinical AD and post-stroke dementia (project 1). As a proof of principle regarding this approach we have already identified genetic variants in A/MPTthat show significant association with both CSF tau and ptau181 levels. Further study shows that this association is limited to individuals with evidence of A(3deposition. Genetic variation in this region also appears to be associated with expression levels of tau mRNA in individuals with amyloid deposition and age at onset of LOAD.